In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor. As entropy coder, the sample-adaptive entropy coder defined in the 123.0-B-1 standard or the low-complexity block-adaptive encoder defined by the CCSDS 121.0-B-2 lossless standard could be used. Both IPs, 123.0-B-1 and 121.0-B-2, are part of SHyLoC 2.0, and can be used together for compression of hyperspectral images, being also possible the compression of any kind of data using only the 121-IP. SHyLoC 2.0 improves and extends the capabilities of SHyLoC 1.0, currently available at the ESA IP Cores library, increasing its compression efficiency and throughput, without compromising the resources footprint. Moreover, it incorporates new features, such as the unit-delay predictor option defined by the CCSDS 121.0-B-2 standard, and burst capabilities in the external memory interface of the CCSDS 123-IP, among others. Dedicated architectures have been designed for all the possible input image sample arrangements, in order to maximise throughput and reduce the hardware resources utilization. The design is technology-agnostic, enabling the mapping of the VHDL code in different FPGAs or ASICs. Results are presented for a representative group of well-known space-qualified FPGAs, including the new NanoXplore BRAVE family. A maximum throughput of 150 MSamples/s is obtained for Xilinx Virtex XQR5VFX130 when the SHyLoC 2.0 CCSDS-123 IP is configured in Band-Interleaved by Pixel (BIP) order, using only the 4% of LUTs and less than the 1% of internal memory.

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

The increment in the use of high-resolution imaging sensors on-board satellites motivates the use of on-board image compression, mainly due to restrictions in terms of both hardware (computational and storage resources) and downlink bandwidth with the ground. This work presents a compression solution based on the CCSDS 123.0-B-2 near-lossless compression standard for multi- and hyperspectral images, which deals with the high amount of data acquired by these next-generation sensors. The proposed approach has been developed following an HLS design methodology, accelerating design time and obtaining good system performance. The compressor is comprised by two main stages, a predictor and a hybrid encoder, designed in Band-Interleaved by Line (BIL) order and optimized to achieve a trade-off between throughput and logic resources utilization. This solution has been mapped on a Xilinx Kintex UltraScale XCKU040 FPGA and targeting AVIRIS images, reaching a throughput of 12.5 MSamples/s and consuming only the 7% of LUTs and around the 14% of dedicated memory blocks available in the device. To the best of our knowledge, this is the first fully-compliant hardware implementation of the CCSDS 123.0-B-2 near-lossless compression standard available in the state of the art.

This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo³, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.

Hyperspectral images can comprise hundreds of spectral bands, which means that they can represent a large volume of data difficult to manage with the available on-board resources. Lossless compression solutions are interesting for reducing the amount of information stored or transmitted while preserving it at the same time. The Hyperspectral Lossless Compressor for space applications (SHyLoC), which is part of the European Space Agency (ESA) IP core’s library, has been demonstrated to meet the requirements of space missions in terms of compression efficiency, low complexity and high throughput. Currently, there is a trend to use Commercial Off-The-Shelf (COTS) on-board electronic devices on small satellites. Moreover, commercial Field-Programmable Gate Arrays (FPGAs) have been used in a number of them. Hence, a reliability analysis is required to ensure the robustness of the applications to Single Event Upsets (SEUs) in the configuration memory. In this work, we present a reliability analysis of this hyperspectral image compression module as a first step towards the development of ad-hoc fault-tolerant protection techniques for the SHyLoC IP core. The reliability analysis is performed using a fault-injection-based experimental set-up in which a hardware implementation of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless compression standard is tested against configuration memory errors in a Xilinx Zynq XC7Z020 System-on-Chip. The results obtained for unhardened and redundancy-based protected versions of the module are put into perspective in terms of area/power consumption and availability/protection coverage gained to provide insight into the development of more efficient knowledge-based protection schemes.

The increment in the data rate of the new-generation hyperspectral sensors is making more critical the necessity of lossy compression solutions able to achieve higher compression ratios (CRs). In this paper, a transform-based lossy compressor, namely lossy compression algorithm for hyperspectral image systems (HW-HyperLCA), is proposed as a modification of the original HyperLCA, specifically designed for being executed into space-grade field programmable gate array (FPGA) devices. The HW-HyperLCA compressor can be totally executed using integer arithmetic, using different levels of precision that can be adapted for achieving the best relation between compression performance and computational burden. The experiments carried out in this paper demonstrate that the HW-HyperLCA compressor is able to compress the hyperspectral images to less than 1 bit per pixel per band with low distortions (more than 40 dB of signal-to-noise ratio) while the performed complexity analysis demonstrates that it can be efficiently executed into space-grade FPGAs using very few computational resources and good results in terms of maximum clock frequency can be obtained.